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Get 0d slices of ndarrays directly from indexing #2625

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Merged
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Dec 22, 2018
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Get 0d slices of ndarrays directly from indexing #2625

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db7a849
Add test to ensure that 0d slices are views
danielwe Dec 21, 2018
9f3fbb2
Get 0d slices of ndarrays directly from indexing
danielwe Dec 21, 2018
26cb008
Add 0d slice documentation
danielwe Dec 21, 2018
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3 changes: 3 additions & 0 deletions doc/whats-new.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -56,6 +56,9 @@ Enhancements
- :py:meth:`DataArray.resample` and :py:meth:`Dataset.resample` now supports the
``loffset`` kwarg just like Pandas.
By `Deepak Cherian <https://github.com/dcherian>`_
- 0d slices of ndarrays are now obtained directly through indexing, rather than
extracting and wrapping a scalar, avoiding unnecessary copying. By `Daniel
Wennberg <https://github.com/danielwe>`_.

Bug fixes
~~~~~~~~~
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16 changes: 5 additions & 11 deletions xarray/core/indexing.py
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Expand Up @@ -1142,15 +1142,6 @@ def __init__(self, array):
'Trying to wrap {}'.format(type(array)))
self.array = array

def _ensure_ndarray(self, value):
# We always want the result of indexing to be a NumPy array. If it's
# not, then it really should be a 0d array. Doing the coercion here
# instead of inside variable.as_compatible_data makes it less error
# prone.
if not isinstance(value, np.ndarray):
value = utils.to_0d_array(value)
return value

def _indexing_array_and_key(self, key):
if isinstance(key, OuterIndexer):
array = self.array
Expand All @@ -1160,7 +1151,10 @@ def _indexing_array_and_key(self, key):
key = key.tuple
elif isinstance(key, BasicIndexer):
array = self.array
key = key.tuple
# We want 0d slices rather than scalars. This is achieved by
# appending an ellipsis (see
# https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#detailed-notes). # noqa
key = key.tuple + (Ellipsis,)
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Two questions:

  1. does this also work in the other branches, i.e., the ones that do fancy indexing?
  2. (related) can you remove the _ensure_ndarray method above?

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  1. It does not affect outer indexing but breaks vectorized indexing, where some arrays come back with dimensions in a different order. I haven't looked into exactly why. It would be nice to move the change into __getitem__, as return array[key + (Ellipsis,)], but I'm afraid that won't work. In any case, I think only basic indexing ever returns a 0d array, so the current change covers all relevant cases.

  2. In fact, yes. I was convinced there were edge cases with object arrays etc., so I let it stay, but all tests pass without. Removed now.

else:
raise TypeError('unexpected key type: {}'.format(type(key)))

Expand All @@ -1171,7 +1165,7 @@ def transpose(self, order):

def __getitem__(self, key):
array, key = self._indexing_array_and_key(key)
return self._ensure_ndarray(array[key])
return array[key]

def __setitem__(self, key, value):
array, key = self._indexing_array_and_key(key)
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5 changes: 5 additions & 0 deletions xarray/tests/test_variable.py
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Expand Up @@ -1147,6 +1147,11 @@ def test_getitem_basic(self):
assert v_new.dims == ('x', )
assert_array_equal(v_new, v._data[:, 1])

# test that we obtain a modifiable view when taking a 0d slice
v_new = v[0, 0]
v_new[...] += 99
assert_array_equal(v_new, v._data[0, 0])

def test_getitem_with_mask_2d_input(self):
v = Variable(('x', 'y'), [[0, 1, 2], [3, 4, 5]])
assert_identical(v._getitem_with_mask(([-1, 0], [1, -1])),
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